code: def testMultiStepEpisodicReplayBuffer(self):
      num_episodes = 5
      num_driver_episodes = 5
  
      # Create mock environment.
      py_env = batched_py_environment.BatchedPyEnvironment(
          [
              driver_test_utils.PyEnvironmentMock(final_state=i + 1)
              for i in range(num_episodes)
          ]
      )
      env = tf_py_environment.TFPyEnvironment(py_env)
  
      # Creat mock policy.
      policy = driver_test_utils.TFPolicyMock(
          env.time_step_spec(),
          env.action_spec(),
          batch_size=num_episodes,
      )
  
      # Create replay buffer and driver.
      replay_buffer = self._make_replay_buffer(env)
      stateful_buffer = (
          episodic_replay_buffer.StatefulEpisodicReplayBuffer(
              replay_buffer, num_episodes
          )
      )
      driver = dynamic_episode_driver.DynamicEpisodeDriver(
          env,
          policy,
          num_episodes=num_driver_episodes,
          observers=[stateful_buffer.add_batch],
      )
  
      run_driver = driver.run()
  
      end_episodes = replay_buffer._maybe_end_batch_episodes(
          stateful_buffer.episode_ids, end_episode=True
      )
  
      completed_episodes = replay_buffer._completed_episodes()
  
      self.evaluate(
          [
              tf.compat.v1.local_variables_initializer(),
              tf.compat.v1.global_variables_initializer(),
          ]
      )
  
      self.evaluate(run_driver)
  
      self.evaluate(end_episodes)
      completed_episodes = self.evaluate(completed_episodes)
      eps = [
          replay_buffer._get_episode(ep) for ep in completed_episodes
      ]
      eps = self.evaluate(eps)
  
      episodes_length = [tf.nest.flatten(ep)[0].shape[0] for ep in eps]
  
      # Compare with expected output.
      self.assertAllEqual(completed_episodes, [3, 4, 5, 6, 7])
      self.assertAllEqual(episodes_length, [4, 4, 2, 1, 1])
  
      first = ts.StepType.FIRST
      mid = ts.StepType.MID
      last = ts.StepType.LAST
  
      step_types = [ep.step_type for ep in eps]
      observations = [ep.observation for ep in eps]
      rewards = [ep.reward for ep in eps]
      actions = [ep.action for ep in eps]
  
      self.assertAllClose(
          [
              [first, mid, mid, last],
              [first, mid, mid, mid],
              [first, last],
              [first],
              [first],
          ],
          step_types,
      )
  
      self.assertAllClose(
          [
              [0, 1, 3, 4],
              [0, 1, 3, 4],
              [0, 1],
              [0],
              [0],
          ],
          observations,
      )
  
      self.assertAllClose(
          [
              [1, 2, 1, 2],
              [1, 2, 1, 2],
              [1, 2],
              [1],
              [1],
          ],
          actions,
      )
  
      self.assertAllClose(
          [
              [1, 1, 1, 0],
              [1, 1, 1, 1],
              [1, 0],
              [1],
              [1],
          ],
          rewards,
      )
  
file_path: agents/tf_agents/replay_buffers/episodic_replay_buffer_driver_test.py
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import tensorflow
  from tf_agents.drivers import dynamic_episode_driver
  from tf_agents.drivers import test_utils
  from tf_agents.environments import batched_py_environment
  from tf_agents.environments import tf_py_environment
  from tf_agents.replay_buffers import episodic_replay_buffer
  from tf_agents.specs import tensor_spec
  from tf_agents.trajectories import policy_step
  from tf_agents.trajectories import time_step
  from tf_agents.trajectories import trajectory
  from tf_agents.utils import test_utils
label_desc: Use image node_id 2 for calling the EpisodicReplayBufferDriverTest obj's underlying member method code with example usage: obj.testMultiStepEpisodicReplayBuffer() without return types